A “Data Revolution” of the People, by the People, and for the People – Not Just for Advocates

African governments suffer from a “statistical tragedy” due to the lack of basic national statistics, as argued in a recent paper and earlier blog post by Shanta Devarajan and in the forthcoming Data for African Development report from a CGD working group. Instead of addressing this institutional weakness, much of the discussion around the High Level Panel’s proposed “data revolution” seems to have devolved into calls for more data, global baselines, and an attempt to establish a list of key indicators to replace the 2015 Millennium Development goals. (See blog posts by my colleagues Amanda Glassman last July and last month and by Nandini Oomann in 2010.) Too often this discussion is driven by advocates for particular kinds of data, as my colleague Victoria Fan has argued.

To the extent that information on a short list of indicators will sharpen the accountability of donors and national governments for improvement on key dimensions of development, focusing on this list will serve a useful role. But to best serve the current and future development needs of citizens’ in low- and middle- income countries, any movement around a data revolution must also support core data and statistical systems used by policymakers to design, implement, and evaluate national policies. After all, the purpose of improved statistical policies and systems should be to assure that national governments succeed in producing the public goods and basic services required to achieve the next generation of development goals.

I recently attended a meeting hosted by PARIS21, a global partnership on development data based at the OECD, to discuss and comment on the launch of a new study which will assess the current capacity of statistical systems in a sample of 20 developing countries and propose a “road map” on how to improve statistical programs worldwide. Such a road map could provide a framework broad enough to encompass the many and varied development purposes of national statistics programs and help the data revolution avoid serving only the most reductionist interpretation of the post-2015 development agenda – the mere checking off of indicator boxes.

In my view, the study’s assessment of current statistical capacity should aim to do two things: define functions of government that are key statistical domains, and conduct a survey of statistical capacity by government function.

Of course, views of the role of government vary from limited to more expansive. But I think most of us would agree that a broadly representative, competent, growth-enabling, poverty-reducing government should protect, support, and use statistics to monitor, at least to some degree, each of the ten functions on this list from the UN statistics office:

1. General public services

2. Defense

3. Public order and safety

4. Economic affairs

5. Environmental protection

6. Housing and community amenities

7. Health

8. (Protection and support of) Recreation, culture and religion

9. Education

10. Social protection

Although none of the UN’s ten “functions” corresponds to any single “indicator” on any list of proposed post-2015 development goals, each element of the above list corresponds to what might be called a “domain” of statistics containing many individual indicators. On the UN’s web site, clicking on any of these functions drills down to subcategories. For example, clicking on the 7th function, “Health”, yields a list of 6 sub-categories of government functions, each of which a government might aspire to monitor with a sub-domain of statistics.

7.1 - Medical products, appliances and equipment

7.2 - Outpatient services

7.3 - Hospital services

7.4 - Public health services

7.5 - R&D Health

7.6 - Health n.e.c.

No sign yet of the very specific kinds of indicators that are on the post-2015 agenda. But then clicking on “07.4 – Public health services” gets us to the following description:

preparation and dissemination of information on public health matters.
Includes: public health services delivered by special teams to groups of clients, most of whom are in good health, at workplaces, schools or other non-medical settings; public health services not connected with a hospital, clinic or practitioner; public health services not delivered by medically qualified doctors; public health service laboratories.

Aha! Now we see mention of some of the post-2015 style indicators. The statistics in this category include data not only on immunization, malnutrition, and family planning (indicators of perennial interest to donors), but also on blood-bank operation, a critical public health service with important public good characteristics that to my knowledge has never been mentioned in any post-2015 discussion.

With these government functions and the corresponding indicators in mind, the study should survey national statistical capacity (both public and private) to collect, curate, and analyze the corresponding indicators in each of the identified domains. In any sampled country, this might start with a list of statistics that are currently being produced, and key facts about those statistics; is it derived from a census, a sample survey, remote sensing, expert opinion or model-based extrapolation? At what subnational level is the statistic available? Which statistical agency or survey organization collects, curates, analyzes, and archives the underlying data? Is that statistic publicly available and if so how? (The World Bank’s “Bulletin Board on Statistical Capacity” can provide some of this metadata.) If the answer to one or more of these questions is unavailable, it suggests a lack of capacity in the corresponding domain.

Building on their assessment of capacity in the 20 countries, PARIS21 will outline a proposed “road map” of statistical development, which they hope could be a useful guide to subsequent statistical strengthening programs. While the assessment produced in the first part of their study would identify weaknesses in the statistics currently collected in each statistical domain, no investment program could immediately redress all identified deficiencies in the capacity to collect, compile, curate, analyze, and disseminate all the desirable indicators. How should the gaps be prioritized?

An important objective of this “road map” would be to measure and strengthen the ability of a country to produce the post-2015 development indicators. But other criteria must be considered. Basic concepts from the “value of information” field, such as the improvement in the expected outcome that can be attributed to a statistic, should guide the prioritization of a statistical capacity investment program. At least equal weight should be given to national decisions (e.g. which neighborhoods to target for clean water provision or HIV prevention) as compared to international ones (e.g. which countries are falling behind on hunger or poverty measures). Finally, the road map should lay out how the national systems will be strengthened to produce the highest priority statistics in all ten domains, with the reliability, timeliness and granularity that a middle income country will require.

As discussions around a “data revolution” continue, ministers of finance and planning in low- and middle- income countries should resist advocate and donor pressure to focus exclusively on a short list of indicators – insisting instead that producing these indicators be a by-product of a stronger and more comprehensive national statistics data collection process. Structuring the data revolution around a limited set of indicators of interest to the post-2015 development constituency does a disservice to the countries and ultimately to the advocates and donors themselves.